Phenobot - Intelligent photonics for molecular phenotyping in Precision Viticulture

The Phenobot platform is comprised by an autonomous robot, instrumentation, artificial intelligence, and digital twin diagnosis at the molecular level, marking the transition from pure data-driven to knowledge-driven agriculture 4.0, towards a physiology-based approach to precision viticulture. Such is achieved by measuring the plant metabolome ‘in vivo' and 'in situ', using spectroscopy and artificial intelligence for quantifying metabolites, e.g.: i. grapes: chlorophylls a and b, pheophytins a and b, anthocyanins, carotenoids, malic and tartaric acids, glucose and fructose; ii. foliage: chlorophylls a and b, pheophytins a and b, anthocyanins, carotenoids, nitrogen, phosphorous, potassium, sugars, and leaf water potential; and iii. soil nutrients (NPK). The geo-referenced metabolic information of each plant (organs and tissues) is the basis of multi-scaled analysis: i. geo-referenced metabolic maps of vineyards at the macroscopic field level, and ii. genome-scale 'in-silico' digital twin model for inferential physiology (phenotype state) and omics diagnosis at the molecular and cellular levels (transcription, enzyme efficiency, and metabolic fluxes). Genome-scale 'in-silico' Vitis vinifera numerical network relationships and fluxes comprise the scientific knowledge about the plant's physiological response to external stimuli, being the comparable mechanisms between laboratory and field experimentation - providing a causal and interpretable relationship to a complex system subjected to external spurious interactions (e.g., soil, climate, and ecosystem) scrambling pure data-driven approaches. This new approach identifies the molecular and cellular targets for managing plant physiology under different stress conditions, enabling new sustainable agricultural practices and bridging agriculture with plant biotechnology, towards faster innovations (e.g. biostimulants, anti-microbial compounds/mechanisms, nutrition, and water management). Phenobot is a project under the Portuguese emblematic initiative in Agriculture 4.0, part of the Recovery and Resilience Plan (Ref. PRR: 190 Ref. 09/C05-i03/2021 – PRR-C05-i03-I-000134)

IgG glycosylation and DNA methylation are interconnected with smoking

Background: Glycosylation is one of the most common post-translation modifications with large influences on protein structure and function. The effector function of immunoglobulin G (IgG) alters between pro- and anti-inflammatory, based on its glycosylation. IgG glycan synthesis is highly complex and dynamic. Methods: With the use of two different analytical methods for assessing IgG glycosylation, we aim to elucidate the link between DNA methylation and glycosylation of IgG by means of epigenome-wide association studies. In total, 3000 individuals from 4 cohorts were analyzed. Results: The overlap of the results from the two glycan measurement panels yielded DNA methylation of 7 CpG-sites on 5 genomic locations to be associated with IgG glycosylation: cg25189904 (chr.1, GNG12); cg05951221, cg21566642 and cg01940273 (chr.2, ALPPL2); cg05575921 (chr.5, AHRR); cg06126421 (6p21.33); and cg03636183 (chr.19, F2RL3). Mediation analyses with respect to smoking revealed that the effect of smoking on IgG glycosylation may be at least partially mediated via DNA methylation levels at these 7 CpG-sites. Conclusion: Our results suggest the presence of an indirect link between DNA methylation and IgG glycosylation that may in part capture environmental exposures. General significance: An epigenome-wide analysis conducted in four population-based cohorts revealed an association between DNA methylation and IgG glycosylation patterns. Presumably, DNA methylation mediates the effect of smoking on IgG glycosylation